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As environmental problems such as global warming are emerging, regulations on automobile exhaust gas are strengthened and various exhaust gas reduction technologies are being developed in various countries in order to satisfy exhaust emission regulations. Exhaust gas recirculation (EGR) technology is a very effective way to reduce nitrogen oxides (NOx) at high combustion temperatures by using EGR coolers to lower the combustion temperature. This EGR cooler has been mass-produced in stainless steel, but it is expensive and heavy. Recently, high efficiency and compactness are required for the EGR cooler to meet the new emission regulation. If aluminum material is applied to the EGR cooler, heat transfer efficiency and light weight can be improved due to high heat transfer coefficient of aluminum compared to conventional stainless steel, but durability is insufficient. Therefore, the aluminum EGR cooler has been developed to enhance performance and durability.

CO2 emission is more serious in recent years and automobile manufacturers are interested in developing technologies to reduce CO2 emissions. Among various environmental-technologies, the use of solar roof as an electric energy source has been studied extensively. For example, in order to reduce the cabin ambient temperature, automotive manufacturers offer the option of mounting a solar cell on the roof of the vehicle [1]. In this paper, we introduce the semi-transparent solar cell mounted on a curved roof glass and we propose a solar energy management system to efficiently integrate the electricity generated from the solar roof into internal combustion engine (ICE) vehicles. In order to achieve a high efficiency solar system in different driving, we improve the usable power other than peak power of solar roof. Peak power or rated power is measured power (W) in standard test condition (@ 25°C, light intensity of 1000W/m2(=1Sun)).

This paper examines the effect of pulse-and-glide (PnG) driving strategies on the fuel efficiency when applied on parallel HEVs. Several PnG strategies are proposed, and these include the electrical, mechanical, and combined PnG strategies. The electrical PnG strategy denotes the hybrid powertrain control tactics in which the battery is charged or discharged according to the power demanded while maintaining the constant vehicle speed. On the other hand, the mechanical PnG strategy denotes the powertrain control tactics in which the vehicle accelerates or decelerates according to the power load while minimizing the battery usage. The combined PnG strategy involves both electrical and mechanical strategies to find a balanced point in between them. Here, a tradeoff relationship between the fuel efficiency and the vehicle drivability related to the tracking performance of the desired target speed is revealed.

A physics-based model for SCR (Selective Catalytic Reduction) was developed based on five independent SGB (Synthetic Gas Bench) tests. There are NH3 adsorption & desorption test, NO oxidation test, NH3 oxidation test, SCR reaction (NOx & NH3) test and SV (Space Velocity) test. To validate the accuracy of SCR model’s prediction, transient reactor tests were conducted at four different input conditions. A newly developed SCR model showed more than 90% prediction accuracy in transient test conditions in view of cumulative NOx. Validation of SCR model was conducted on 1.6L light duty diesel vehicle in the WLTC (Worldwide Harmonized Light vehicles Test Cycle). Based upon this SCR model, vehicle level SCR calibrations used for urea dosing control were made and validated in the emission test cycles like WLTC.

Electric Power Steering (EPS) needs to meet both functional and stability requirements, it plays significant role in controlling vehicle motion. In the meantime, customers emphasizes natural steering feel which can reflect vehicle motion and road surface information while isolate unwanted external disturbances. In general, conventional EPS control algorithms exert assist torque according to driver torque measured from torque sensor, while maintaining stability using stabilizing compensator. However, there exist significant trade-off between steering feel and stability, because the performances of assist torque control and stabilizing compensator are strongly coupled. In this paper a torque feedback control algorithm for EPS system is proposed in order to overcome the trade-off, and to achieve more natural, robust steering feel.

This paper describes a reference steering feel tracking algorithm for Electric-Power-Steering (EPS) system. Development of the EPS system with intended steering feel has been time-consuming procedure, because the feedforward map-based method has been applied to the conventional EPS system. However, in this study, a three-dimensional reference steering feel surface, which is determined from current vehicle states, is proposed. In order to track the proposed reference steering feel surface, sliding mode approach is applied to second-order steering dynamics model considering a coulomb friction model. An adaptive technique is utilized for robustness against uncertainties. In order to validate the proposed EPS control algorithm, hardware-in-the-loop simulation (HILS) has been conducted with respect to a typical steering test. It is shown that the reference steering feel is realized well by the proposed EPS control algorithm.

The method of Front End Auxiliary Drive (FEAD) system optimization can be divided into two ways. One is to use a mechanical device that decouples crank pulley from torsional vibration of crank shaft by using characteristics of spring. The other is to control belt tension through auto-tensioner in addition of alternator pulley device. Because the former case has more potential to reduce belt tension than the latter case, the development of mechanically decoupled crank pulley, despite of its difficulty of development, is getting popular among the industry. This paper characterizes latest crank pulley technologies, Crank Decoupler and Isolation Pulley, for torsional vibration reduction through functionality measurement result which composed of irregularity, slip, tensioner movement, belt span vibration, bearing hubload of idler and so on. Also it investigates their potential of belt tension reduction through steady state point fuel consumption test on dynamometer.

In diesel engine development, the new technology is coming out to meet the stringent exhaust emission regulation. The regulation demands more eco-friendly vehicles. Euro6c demands to meet not only WLTP mode, but also RDE(Real Driving Emission). In order to satisfy RDE mode, the new technology to reduce emissions should cover all operating areas including High Load & High Speed. It is a big challenge to reduce NOx on the RDE mode and a lot of DeNOx technologies are being developed. So the new DeNOx technology is needed to cover widened operating area and strict acceleration / deacceleration. The existing LNT(Lean NOx Trap) and Urea SCR(Selective Catalytic Reduction) is necessary to meet the typical NEDC or WLTP, but the RDE mode demands the powerful DeNOx technology. Therefore, the LNT & Urea SCR on DPF was developed through this study.

Road friction estimation algorithms had been studied for many years because it is very important factor for safety control and fuel efficiency of vehicle. But traditional solutions are hard to adapt in automotive industry because their performance is not sufficient enough and expensive to implement. Therefore, this paper proposes a road friction estimation algorithm based on a trained artificial neural-network which is low cost and robust. The suggested method doesn’t need expensive additional sensors such as optical or lidar sensor, also it shows better performance in real car environment compared to other algorithms based on vehicle dynamics. In this paper, we would describe this algorithm in detail and analyze the test results evaluated in real road conditions.

Recently, as part of the effort to enhance fuel efficiency and reduce costs for eco-friendly vehicles, the R-gearless system has been implemented in the TMED (P)HEV system. Due to the removal of the reverse gear, a distinct backward driving method needs to be developed, allowing the Electronic Motor (e-Motor) system to facilitate backward movement in the TMED (P)HEV system. However, the capability of backward driving with the e-Motor is limited because of partial failure in the high-voltage system of an R-gearless system. Thus, we demonstrate that it is possible to improve backward driving problems by applying a new fail-safe strategy. In the event of a high-voltage battery system failure, backward driving can be achieved using the e-Motor with constant voltage control by the Hybrid Starter Generator (HSG), as proposed in this study.

Broadband active noise control algorithms require high-performance so multi-channel control to ensure high performance, which results in very high computational power and expensive DSP. When the control filter update part need a huge computational power of the algorithm is separated and calculated by the server, it is possible to reduce cost by using a low-cost DSP in a local vehicle, and a performance improvement algorithm requiring a high computational power can be applied to the server. In order to achieve the above goal, this study analyzed the maximum delay time when communication speed is low and studied response measures to ensure data integrity at the receiving location considering situations where communication speed delay and data errors occur.

Since the fuel efficiency of vehicle has become one of the big issues due to environmental pollution problems, many studies have been conducted on various methods such as improving powertrain performance and aerodynamic performance, reducing the weight of the vehicle and so on. There have been many new attempts to reduce weight but mostly about improving material property. In the case of vehicles sharing the same platform, the weight and cost of vehicle are mainly changed by the exterior styling. But, there is no solution to control the exterior styling in terms of the weight and cost of vehicle, yet. The purpose of this study is to find the way to save the weight and cost of vehicle while achieving the various performance and requirements of vehicle (safety, aerodynamics, driver’s visibility and so on) from exterior styling point of view. We focused on the weight difference of the vehicles that shared the platform and were same overall dimensions.

After the COVID-19 pandemic, leisure activities and cultures have undergone significant transformations. Particularly, there has been an increased demand for outdoor camping. Consequently, the need for capabilities that allow vehicles to navigate not only paved roads but also unpaved and rugged terrains has arisen. In this study, we aim to address this demand by utilizing AI to introduce a 'Stuck Probability Estimation Algorithm' for vehicles on off-road. To estimate the 'Stuck Probability' of a vehicle, a mathematical model representing vehicle behavior is essential. The behavior of off-road driving vehicles can be characterized in two main aspects: firstly, the harshness of the terrain (how uneven and rugged it is), and secondly, the extent of wheel slip affecting the vehicle's traction.

The high-frequency whining noise produced by motors in modern electric vehicles causes a significant issue, leading to annoyance among passengers. This noise becomes even more noticeable due to the quiet nature of electric vehicles, which lack other noises to mask the high-frequency whining noise. To improve the noise caused by motors, it is essential to optimize various motor design parameters. However, this task requires expert knowledge and a considerable time investment. In this study, we explored the application of artificial intelligence to optimize the NVH performance of motors during the design phase. Firstly, we selected and modeled three benchmark motor types using Motor-CAD. Machine learning models were trained using Design of Experiment methods to simulate batch runs of Motor-CAD inputs and outputs.

It is an important factor in electric vehicles to show customers how much they can drive with the energy of the remaining battery. If the remaining mileage is not accurate, electric vehicle drivers will have no choice but have to feel anxious about the mileage. Additionally, the potential customers have range anxiety when they consider Electric Vehicles. If the remaining mileage to drive is wrong, drivers may not be able to get to the charging station and may not be able to drive because the battery runs out. It is important to show the remaining available driving range exactly for drivers. The previous study proposed an advanced model by predicting the remaining mileage based on actual driving data and based on reflecting the pattern of customers who drive regularly. The Bayesian linear regression model was right model in previous study.

Pressure variation during engine combustion generates torque fluctuation that is delivered through the driveline. Torque fluctuation delivered to the tire shakes the vehicle body and causes the body components to vibrate, resulting in booming noise. HKMC (Hyundai Kia Motor Company)’s TMED (Transmission Mounted Electric Device) type generates booming noises due to increased weight from the addition of customized hybrid parts and the absence of a torque converter. Some of the improvements needed to overcome this weakness include reducing the torsion-damper stiffness, adding dynamic dampers, and moving the operation point of the engine from the optimized point. These modifications have some potential negative impacts such as increased cost and sacrificed fuel economy. Here, we introduce a method of reducing lock-up booming noise in an HEV at low engine speed.

To improve the fuel efficiency and satisfy the strict emission regulations, the development of internal combustion engine gets more complicated in both hardware and software perspectives, and the margins for durability and NVH quality become narrower, which could result in poor NVH robustness in harsh engine operating conditions. In this paper, we investigate experimentally the camshaft impact noise mechanism relating the valve train and timing chain forces to detailed motion of the camshaft and the chain tensioner. After the initial investigation of identifying the impact timings and specific engine operating points when the noise occurs, the camshaft orbital motion inside of the sliding bearing is measured and visualized with the proximity sensors with calibration after sensor mounting, in addition to the chain tensioner movements.

A systematic process of optimization is suggested to obtain the best control maps for a parallel type hybrid electric vehicle. Taking the fuel consumption as the cost function and driving cycle as part of the constraints, an optimization problem for CVT pulley ratio control and motor torque control can be formulated. The change of the battery charge state between the start and end point of the given driving cycle also works as a constraint. In order to see the effect of various control strategies on system behavior and overall fuel consumption, a simulation model was built to accommodate the functional blocks representing hybrid powertrain subsystem components and corresponding control units.

Reductions in powertrain noise have led to an increased proportion of road noise, prompting various studies aimed at mitigating it. Road excitation primarily traverses through the vehicle suspension system, necessitating careful optimization of the characteristics of bushings at connection points. However, optimizing at the vehicle assembly stage is both time-consuming and costly. Therefore, it is essential to proceed with optimization at the subsystem level using appropriate objective functions. In this study, the blocked force and energy-based index derived from complex power were used to optimize the NVH performance. Calculating the complex power in each bushing enables computing the power flow, thereby providing a basis for evaluating the NVH performance. Through stiffness injection, the frequency response functions (FRF) of the system can be predicted according to arbitrary changes in the bushing stiffness.

In this study, a co-operative regenerative braking control algorithm was developed for an electric vehicle (EV) equipped with an electronic wedge brake (EWB) for its front wheels and an electronic mechanical brake (EMB) for its rear wheels. The co-operative regenerative braking control algorithm was designed considering the road friction characteristic to increase the recuperation energy while avoiding wheel lock. A powertrain model of an EV composed of a motor, and batteries and a MATLAB model of the control algorithm were also developed. They were linked to the CarSim model of the vehicle under study to develop an EV simulator. The EMB and EWB were modeled with an actuator, screw, and wedge to develop an EMB and EWB simulator. A co-simulator for an EV equipped with an EWB for the front wheels and an EMB for the rear wheels was fabricated, composed of the EV and the EMB and EWB simulator.